College of Medicine, University of Florida, Gainesville, FL, USA; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Clin Neurol Neurosurg. 2024 Nov;246:108521. doi: 10.1016/j.clineuro.2024.108521. Epub 2024 Aug 30.
The escalating healthcare expenditures in the United States, particularly in neurosurgery, necessitate effective tools for predicting patient outcomes and optimizing resource allocation. This study explores the utility of combining frailty and comorbidity indices, specifically the Johns Hopkins Adjusted Clinical Groups (JHACG) frailty index and the Elixhauser Comorbidity Index (ECI), in predicting hospital length of stay (LOS), non-routine discharge, and one-year readmission in patients undergoing craniotomy for benign and malignant primary brain tumors.
Leveraging the Nationwide Readmissions Database (NRD) for 2016-2019, we analyzed data from 645 patients with benign and 30,991 with malignant tumors. Frailty, ECI, and frailty + ECI were assessed as predictors using generalized linear mixed-effects models. Receiver operating characteristic (ROC) curves evaluated predictive performance.
Patients in the benign tumor cohort had a mean LOS of 8.1 ± 15.1 days, and frailty + ECI outperformed frailty alone in predicting non-routine discharge (AUC 0.829 vs. 0.820, p = 0.035). The malignant tumor cohort patients had a mean LOS of 7.9 ± 9.1 days. In this cohort, frailty + ECI (AUC 0.821) outperformed both frailty (AUC 0.744, p < 0.0001) and ECI alone (AUC 0.809, p < 0.0001) in predicting hospital LOS. Frailty + ECI (AUC 0.831) also proved superior to frailty (AUC 0.809, p < 0.0001) and ECI alone (AUC 0.827, p < 0.0001) in predicting non-routine discharge location for patients with malignant tumors. All indices performed comparably to one another as a predictor of readmission in both cohorts.
This study highlights the synergistic predictive capacity of frailty + ECI, especially in malignant tumor cases, and further suggests that comorbid diseases may greatly influence perioperative outcomes more than frailty. Enhanced risk assessment could aid clinical decision-making, patient counseling, and resource allocation, ultimately optimizing patient outcomes.
美国医疗保健支出不断攀升，尤其是神经外科领域，因此需要有效的工具来预测患者的预后并优化资源配置。本研究探讨了结合衰弱和合并症指数（特别是约翰霍普金斯调整临床分组（JHACG）衰弱指数和 Elixhauser 合并症指数（ECI））在预测行开颅手术的良性和恶性原发性脑肿瘤患者的住院时间（LOS）、非常规出院和一年再入院方面的效用。
利用 2016 年至 2019 年的全国再入院数据库（NRD），我们分析了 645 例良性肿瘤患者和 30991 例恶性肿瘤患者的数据。使用广义线性混合效应模型评估衰弱、ECI 和衰弱+ECI 作为预测因子。接收者操作特征（ROC）曲线评估预测性能。
良性肿瘤组患者的平均 LOS 为 8.1±15.1 天，衰弱+ECI 在预测非常规出院方面优于衰弱单独预测（AUC 0.829 与 0.820，p=0.035）。恶性肿瘤组患者的平均 LOS 为 7.9±9.1 天。在该队列中，衰弱+ECI（AUC 0.821）在预测 LOS 方面优于衰弱（AUC 0.744，p<0.0001）和 ECI 单独（AUC 0.809，p<0.0001）。衰弱+ECI（AUC 0.831）在预测恶性肿瘤患者非常规出院地点方面也优于衰弱（AUC 0.809，p<0.0001）和 ECI 单独（AUC 0.827，p<0.0001）。所有指数在两个队列中作为再入院的预测因子表现相当。
本研究强调了衰弱+ECI 的协同预测能力，特别是在恶性肿瘤病例中，进一步表明合并症可能比衰弱更能显著影响围手术期结局。增强风险评估可以辅助临床决策、患者咨询和资源配置，最终优化患者预后。
